Principled multi-omic analysis reveals gene regulatory mechanisms of phenotype variation

Plate diagram of pGENMi model. A latent variable z_g represents whether a gene g mediates the influence of a TF on phenotype, and its enclosing rectangle denotes G such genes. p_g denotes the TWAS P-value between the gene's expression and phenotype variation for each of the G genes. If z_g = 1, we expect an enrichment for significant TWAS P-values, and p_g is modeled by a Beta distribution parameterized by α; otherwise, p_g is modeled as being distributed uniformly in [0,1]. We also observe one or more lines of evidence supporting the TF's influence on the expression of each gene g, such as the existence of a cis-eQTL within a ChIP peak of the TF near that gene. These “regulatory evidences” are denoted by the binary variables r_gm, and there exist M such types of evidence (m = 1 … M), with relative weights w_m. These evidences combine in a logistic function to determine Pr(z_g = 1). The weights w_m are learned over all genes, and as such, are shown outside of the rectangle enclosing G. The H variable indicates whether w_m is free or restricted to zero (null model) for hypothesis testing (see “Probabilistic graphical model” section in Methods).